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Overview of CLEF NEWSREEL 2014: news recommendation evaluation labs
Kille, Benjamin; Brodt, Torben; Heintz, Tobias; Hopfgartner, Frank; Lommatzsch, Andreas; Seiler, Jonas
This paper summarises objectives, organisation, and results of the first news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaignstyle evaluation lab. Participants had the chance to apply two types of evaluation schemes. On the one hand, participants could apply their algorithms onto a data set. We refer to this setting as off-line evaluation. On the other hand, participants could deploy their algorithms on a server to interactively receive recommendation requests. We refer to this setting as on-line evaluation. This setting ought to reveal the actual performance of recommendation methods. The competition strived to illustrate differences between evaluation with historical data and actual users. The on-line evaluation does reflect all requirements which active recommender systems face in practise. These requirements include real-time responses and large-scale data volumes. We present the competition’s results and discuss commonalities regarding participants’ approaches.
Published in: CLEF 2014, CLEF2014 working notes : working notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014, RWTH
